Practical Characteristics of Neural Network and Conventional Pattern Classifiers on Artificial and Speech Problems

Abstract

Eight neural net and conventional pattern classifiers (Bayesian(cid:173) unimodal Gaussian, k-nearest neighbor, standard back-propagation, adaptive-stepsize back-propagation, hypersphere, feature-map, learn(cid:173) ing vector quantizer, and binary decision tree) were implemented on a serial computer and compared using two speech recognition and two artificial tasks. Error rates were statistically equivalent on almost all tasks, but classifiers differed by orders of magnitude in memory requirements, training time, classification time, and ease of adaptivity. Nearest-neighbor classifiers trained rapidly but re(cid:173) quired the most memory. Tree classifiers provided rapid classifica(cid:173) tion but were complex to adapt. Back-propagation classifiers typ(cid:173) ically required long training times and had intermediate memory requirements. These results suggest that classifier selection should often depend more heavily on practical considerations concerning memory and computation resources, and restrictions on training and classification times than on error rate.